Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism - Cong, Dan; Zhao, Yanan; Zhang, Wenlong; Li, Jun; Bai, Yuansong (Frontiers in Pharmacology, 2023)
Background: The progression of lung adenocarcinoma (LUAD) may be related to abnormal fatty acid metabolism (FAM). The present study investigated the relationship between FAM-related genes and LUAD prognosis. Methods: LUAD samples from The Cancer Genome Atlas (TCGA) were collected. The scores of FAM-associated pathways from Kyoto Encyclopedia of Genes and Genomes (KEGG) website were calculated using single sample gene set enrichment analysis (ssGSEA). ConsensusClusterPlus and Cumulative distribution function (CDF) were used to classify molecular subtypes for LUAD. Key genes were obtained using limma package, Cox regression analysis and six machine learning (GBM, lasso, xgboost, SVM, random Forest, Decision Trees) and a RiskScore model was established. According to a RiskScore model and clinical features, a nomogram was developed and evaluated for its prediction performance using calibration curve. Differences of immune abnormalities among patients with different subtypes and RiskScores were analyzed by Estimation of STromal and Immune cells in MAlignant Tumours using Expression (ESTIMATE), CIBERSORT and ssGSEA. Patients' drug sensitivity was predicted by pRRophetic package in R language. Results: LUAD samples had lower scores of FAM-related pathways. Three molecular subtypes (C1, C2, and C3) were defined. Analysis on differential prognosis showed that C1 subtype had the most favorable prognosis, followed by C2 subtype, and C3 subtype had the worst prognosis. C3 subtype had lower immune infiltration. A total of 12 key genes (SLC2A1, PKP2, FAM83A, TCN1, MS4A1, CLIC6, UBE2S, RRM2, CDC45, IGF2BP1, ANGPTL4, CD109) were screened and used to develop a RiskScore model. Survival chance of patients in the high-RiskScore group was significantly lower. The low-RiskScore group showed higher immune score and higher expression of most immune checkpoint genes. Patients with a high RiskScore were more likely to benefit from the six anti-cancer drugs we screened in this study. Conclusions: We developed a RiskScore model using FAM-related genes to help predict LUAD prognosis and develop new targeted drugs.